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Abstract:
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This presentation will offer a legal expert’s overview, for statisticians, of the use of “black box” proprietary algorithms at all stages of criminal justice, from investigation (predictive policing, use of AI to determine falsity of confessions, etc.) to pretrial detention (risk assessment) to proof of guilt/innocence at trial (from probabilistic genotyping software to text-based authorship attribution to facial recognition software), to legal informatics (e.g. “moneyball prosecution”). The presentation will discuss legal issues arising from such proof, including inaccurate verdicts based on unreliable results (discussing how concepts of “hearsay” and “confrontation” might apply to machines, and discussing “Daubert”/”Frye” expert requirements and authentication rules), and pros/cons of reforms such as source code disclosure. The author will also discuss intellectual property and privacy issues that arise with respect to proprietary algorithms, and, time permitting, several other recurring legal issues of likely general interest to statisticians, including quantification of standards of proof, reluctance to allow Bayesian reasoning, and juror understanding of LRs.
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